A team of Russian and Bulgarian scientists discovered in the human brain the neural networks responsible for the onset of paranoia and depression, the Izvestia newspaper reported on May 31.
The current method of identifying paranoia is based on the patient’s answers to specific questions. This method is subjective and not always accurate. The discovery by scientists from the Baltic Federal University Immanuel Kant and the Medical University of Plovdiv will make it possible to complement the questionnaires with functional magnetic resonance imaging (MRI).
60 patients diagnosed with major depressive disorder and schizophrenia participated in the study. Scientists monitored brain activity in real time while patients answered questions of different types: neutral or related to depressive and paranoid symptoms.
“We found markers of neuronal activity associated with depression and paranoia. These are neural networks, that is, interactions between different areas of the brain. Our team first extracts the signal from each area using MRI and then we use mathematical processing to establish connections between them. And based on these characteristics we found differences between different groups of patients.”said IKBFU researcher Semyon Kurkin.
The researcher explained that the conclusion about the presence of paranoia and depression in a patient is usually made based on the number of points obtained when answering questions. However, it is possible to get a borderline score or even a false positive test result. Assessment of a person’s condition using markers can confirm the doctor’s conclusions.
The developers of the methodology plan to continue the research using technologies related to artificial intelligence. Scientists hope that this will increase the accuracy of diagnosis (currently errors occur in 10% of cases). In addition, researchers aim to find the causes of alterations in brain networks, as well as expand the number of mental disorders that can be diagnosed using magnetic resonance imaging.
Source: Rossa Primavera
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